17953397. SYSTEMS AND METHODS FOR DISTRIBUTION-AWARE GOAL PREDICTION FOR MODULAR AUTONOMOUS VEHICLE CONTROL simplified abstract (Robert Bosch GmbH)
Contents
- 1 SYSTEMS AND METHODS FOR DISTRIBUTION-AWARE GOAL PREDICTION FOR MODULAR AUTONOMOUS VEHICLE CONTROL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR DISTRIBUTION-AWARE GOAL PREDICTION FOR MODULAR AUTONOMOUS VEHICLE CONTROL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEMS AND METHODS FOR DISTRIBUTION-AWARE GOAL PREDICTION FOR MODULAR AUTONOMOUS VEHICLE CONTROL
Organization Name
Inventor(s)
Jonathan Francis of Pittsburgh PA (US)
SYSTEMS AND METHODS FOR DISTRIBUTION-AWARE GOAL PREDICTION FOR MODULAR AUTONOMOUS VEHICLE CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 17953397 titled 'SYSTEMS AND METHODS FOR DISTRIBUTION-AWARE GOAL PREDICTION FOR MODULAR AUTONOMOUS VEHICLE CONTROL
Simplified Explanation
The abstract describes a method that involves using a machine learning model to predict current, historical, and future vehicle positions based on sensor data, waypoints, and previous spatial information.
- Machine learning model used for vehicle position prediction
- Predicting current, historical, and future vehicle positions
- Utilizing sensor data and waypoints for prediction
- Controlling vehicle operations based on predictions
Potential Applications
This technology could be applied in various industries such as autonomous vehicles, logistics, transportation, and fleet management.
Problems Solved
This technology helps in improving navigation, route planning, and overall efficiency of vehicle operations.
Benefits
The benefits of this technology include enhanced safety, optimized vehicle routes, reduced fuel consumption, and improved overall performance.
Potential Commercial Applications
One potential commercial application of this technology could be in developing advanced navigation systems for autonomous vehicles.
Possible Prior Art
Prior art in this field may include research on machine learning models for vehicle trajectory prediction and autonomous vehicle technologies.
Unanswered Questions
How does the machine learning model handle real-time data updates during operation?
The abstract does not provide details on how the machine learning model adapts to real-time changes in sensor data and waypoints.
What is the accuracy rate of the vehicle position predictions compared to traditional methods?
The abstract does not mention the accuracy rate of the predictions and how they compare to existing methods.
Original Abstract Submitted
A method includes generating, using a machine learning model and at a first time interval, a first current vehicle position prediction and generating, using the machine learning model, at a second time interval, a first historical vehicle trajectory prediction based on at least the first current vehicle position prediction and previous spatial information. The method also includes generating, using the machine learning model, at a third time interval, a first future vehicle position prediction based on the first current vehicle position prediction and the first historical vehicle trajectory predication. The method also includes receiving, at the first time interval, sensor data and a sequence of waypoints, and controlling, at the first time interval, at least one vehicle operation of the vehicle using the first current vehicle position prediction, the first historical vehicle trajectory prediction, the first future vehicle position prediction, the sensor data, and the sequence of waypoints.
